1 Introduction

In urban areas, efforts to improve air quality often involve creative solutions, such as implementing “school streets” to reduce pollution around schools. This analysis aims to measure the impact of such interventions on air quality by examining changes in pollutant levels during designated intervention periods. Data from twelve schools across eleven wards in Bradford have been collected, focusing on pollutants like Nitric Oxide (NO), Nitrogen Dioxide (NO2), Ozone (O3), PM1, PM10, and PM2.5.

The study initially covers a three-month period, starting from June 8th, 2023, to September 20th, 2023, during which the school street initiative was implemented. This initiative aims to reduce pollution during two key times: the morning drop-off and afternoon pick-up periods. Based on similar programs in London and insights from a local study, the intervention times have been estimated to be from 7:45 AM to 9:15 AM for drop-offs and from 2:30 PM to 4:30 PM for pick-ups, with the intention of refining these times as the data is analyzed.

The analysis is approached from three angles: descriptive, time series, and spatial analyses. Descriptively, a thorough overview of each pollutant’s concentration across all schools will be provided, using common statistical measures like averages, medians, and ranges.

Next, the time aspect will be explored, examining how pollutant levels change over the three-month period. Statistical tests will be employed to determine if there are significant differences in pollutant concentrations between intervention and non-intervention periods.

Finally, spatial variations in air quality will be explored by using interpolation techniques to estimate pollutant concentrations in areas where direct data collection was not possible. By combining these methods, insights can be gained to guide future efforts to improve air quality in urban environments.

2 Document specification

knitr::opts_chunk$set(comment = " ", warning = FALSE, message = "FALSE")

options(knitr.table.format = "html")

3 Loading and cleaning the datasets

## install.packages("gridExtra")
library(tidyverse)
library(sf)
library(kableExtra)
library(stringr)
library(htmlTable)
library(tableone)
library(tmap)
library(paletteer)
library(ggthemes)
library(readxl)
library(Hmisc)
library(stringr)
library(paletteer)
library(gridExtra)
library(grid)
library(htmltools)

# loading all the school street datasets

street_apperley <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Apperley Bridge Marina Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_cavendish <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Cavendish Prim Sch Hall Rd Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_EastBowling <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford East Bowling Flockton Grove Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_Fairfield <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Farfield Prim Sch Reevy Cres Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_Girlington <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Girlington St Leonards Road Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_GT_Horton <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Gt Horton All Saints Prim Sch Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_Horton <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Horton Aberdeen Place Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_Ilkley <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Ilkley Grove Road Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_Keighley <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Keighley Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_low_moor <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Low Moor First Street Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_saltaire <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Saltaire Titus Street Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


street_silsden <- readxl::read_xlsx("/Users/olasho/Desktop/R language2/My projects/Air Quality Project/input_Data/Bradford Silsden Hothfield Street Zephyr 15-Minute Means Mass units 8 June to 20 September 2023.xlsx", skip = 3, col_names = TRUE)


# Ensuring the dates are in UTC

head(street_apperley$Date) 

# Checking for number of empty rows

sum(!complete.cases(street_apperley$`Nitric Oxide µg m-3 (20'C 1013mb)`))

# Creating a function to get rid of empty cells by row

remove_missing_roW <-  function(df, lower_empty_value, higher_empty_value) {
  
  missing_cells <- vector("numeric", length = nrow(df)) # create empty vector
  
  for (i in 1:nrow(df)) {
    missing_cells[i] <- sum(is.na(df[i, ]))                # fill empty vector with number of missing cells per row
  }
  
  df_1 <- df %>%
    mutate(number_of_empty_cells = missing_cells)        # create a new column for the number of empty cells
  
  df_2 <- df_1 %>%
    filter(!(number_of_empty_cells >= lower_empty_value & number_of_empty_cells <= higher_empty_value))          # this filters out the rows with the specified number of empty cells

  df_3 <- subset(df_2, select = -number_of_empty_cells)   # removing the column for number of emtpy cells
  
  return(df_3)                                            # returning the dataframe 
}

# Applying the funtion to all the datasets to get rid of rows with more than 8 empty cells

street_apperley <- remove_missing_roW(street_apperley, 8, 9)

street_cavendish <- remove_missing_roW(street_cavendish, 8, 9)

street_EastBowling <- remove_missing_roW(street_EastBowling, 8, 9)

street_Fairfield <- remove_missing_roW(street_Fairfield, 8, 9)

street_Girlington <- remove_missing_roW(street_Girlington, 8, 9)

street_GT_Horton <- remove_missing_roW(street_Horton, 8, 9)

street_Horton <- remove_missing_roW(street_Horton, 8, 9)

street_Ilkley <- remove_missing_roW(street_Ilkley, 8, 9)

street_Keighley <- remove_missing_roW(street_Keighley, 8, 9)

street_low_moor <- remove_missing_roW(street_low_moor, 8, 9)

street_saltaire <- remove_missing_roW(street_saltaire, 8, 9)

street_silsden <- remove_missing_roW(street_silsden, 8, 9)

# Adding the school street name to each dataset in separate columns

street_apperley$Sites <- "Apperley Bridge marina"

street_cavendish$Sites <- "Cavendish Prim Sch Hall Rd"

street_EastBowling$Sites <- "East Bowling Flockton Grove"

street_Fairfield$Sites <- "Farfield PRim Sch Reevy"

street_Girlington$Sites <- "Girlington St Leonards Road"

street_GT_Horton$Sites <- "Gt Horton All Saints Primary School"

street_Horton$Sites <- "Horton Aberdeen Place"

street_Ilkley$Sites <- "Ilkley Grove Road"

street_Keighley$Sites <- "Keighley"

street_low_moor$Sites <- "Low Moor First"

street_saltaire$Sites <- "Saltaire Titus"

street_silsden$Sites <- "Silsden Hothfield"

# Adding the school street ward to each dataset in separate columns

street_apperley$Ward <- "Idle and Thackley"

street_cavendish$Ward <- "Eccleshill"

street_EastBowling$Ward <- "Bowling and Barkerend"

street_Fairfield$Ward <- "Wibsey"

street_Girlington$Ward <- "Toller"

street_GT_Horton$Ward <- "Great Horton"

street_Horton$Ward <- "Great Horton"

street_Ilkley$Ward <- "Ilkley"

street_Keighley$Ward <- "Keighley West"

street_low_moor$Ward <- "Wyke"

street_saltaire$Ward <- "Shipley"

street_silsden$Ward <- "Craven"

# Adding the 2022 ward code for each ward

street_apperley$Ward_code <- "E05001353"

street_cavendish$Ward_code <- "E05001350"

street_EastBowling$Ward_code <- "E05001345"

street_Fairfield$Ward_code <- "E05001367"

street_Girlington$Ward_code <- "E05001364"

street_GT_Horton$Ward_code <- "E05001351"

street_Horton$Ward_code <- "E05001351"

street_Ilkley$Ward_code <- "E05001354"

street_Keighley$Ward_code <- "E05001357"

street_low_moor$Ward_code <- "E05001370"

street_saltaire$Ward_code <- "E05001362"

street_silsden$Ward_code <- "E05001349"

# Joining all the school streets together

street_all_sites <- rbind(street_apperley, street_cavendish, street_EastBowling, street_Fairfield, street_Girlington, street_GT_Horton, street_Horton, street_Ilkley, street_Keighley, street_low_moor, street_saltaire, street_silsden)

# Reordering the variables so the ward code, ward, and school street come first

street_all_school <- street_all_sites %>%
  select(Ward_code, Ward, Sites, everything())

# Filling the month column with the appropriate month

street_all_sites <- street_all_sites

street_all_sites$Date <- as.character(street_all_sites$Date) # converting date from POSIXct to character

street_all_sites$Month <- ifelse(str_detect(street_all_sites$Date, "06"), "June", ifelse(str_detect(street_all_sites$Date, "07"), "July", ifelse(str_detect(street_all_sites$Date, "08"), "August", ifelse(str_detect(street_all_sites$Date, "09"), "September", "others"))))

# convertign the date back to GMT

street_all_sites$Date <- ymd(street_all_sites$Date, tz = "GMT")

# Separating the date from the time

street_all_sites <- separate(street_all_sites, Hour, into = c("ate", "Hour"), sep = " ") 

# Removing the ate column created

street_all_sites <- subset(street_all_sites, select = -ate)

# Adding the day to the column

street_all_sites$day <- weekdays(street_all_sites$Date)

# Separating out the dataset into weekdays and weekends

street_all_sites_weekday <- street_all_sites %>% 
  filter(day %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))

street_all_sites_weekends <- street_all_sites %>%
  filter(day %in% c("Saturday", "Sunday"))

4 Summary of Air pollutants

This section presents an analysis focused on determining key summary statistics for various air pollutants. The goal is to identify the mean, standard deviation, minimum, and maximum values of each pollutant. Furthermore, we aim to characterize the average pollutant concentrations for each individual school, offering valuable insights into the distribution of pollutants within our study area.

street_all_sites_3 <- street_all_sites_weekday

# Renaming the columns

street_new_column_names <- c("Date", "Month", "Hour", "Temperature \u00B0C", "Atmospheric Pressure", "NO\u2082", "NO", "O\u2083", "PM1", "PM10", "PM2.5", "Site", "Ward", "Ward-Code", "Day")

names(street_all_sites_3) <- street_new_column_names

# Summary table showing the mean of pollutants by each school street; This was carried out individually for each school street because they have varying number of missing values.

## First a function will be created to find the mean of each pollutant for each school street

Street_summary <- function(df, street) {
street_all_sites_3 %>%
  group_by(Site) %>%
  filter(Site == street) %>%
  summarise(mean_NO = mean(NO, na.rm = TRUE),
            mean_NO2 = mean(`NO₂`, na.rm = TRUE),
            mean_O3 = mean(`O₃`, na.rm = TRUE),
            mean_PM1 = mean(PM1, na.rm = TRUE),
            mean_PM10 = mean(PM10, na.rm = TRUE),
            mean_PM25 = mean(`PM2.5`, na.rm = TRUE),
            )
}

## Apperley Bridge marina

street_summary_A <- Street_summary(street_all_sites_3, "Apperley Bridge marina")

## Cavendish

street_summary_B <- Street_summary(street_all_sites_3, "Cavendish Prim Sch Hall Rd")

## East Bowling

street_summary_C <- Street_summary(street_all_sites_3, "East Bowling Flockton Grove")

## Farfield

street_summary_D <- Street_summary(street_all_sites_3, "Farfield PRim Sch Reevy")

## Girlington

street_summary_E <- Street_summary(street_all_sites_3, "Girlington St Leonards Road")

## "Gt Horton All Saints Primary School"

street_summary_F <- Street_summary(street_all_sites_3, "Gt Horton All Saints Primary School")

## "Horton Aberdeen Place"

street_summary_G <- Street_summary(street_all_sites_3, "Horton Aberdeen Place")

## "Ilkley Grove Road"

street_summary_H <- Street_summary(street_all_sites_3, "Ilkley Grove Road")

## "Keighley"

street_summary_I <- Street_summary(street_all_sites_3, "Keighley")

## "Low Moor First" 

street_summary_J <- Street_summary(street_all_sites_3, "Low Moor First")

## "Saltaire Titus" 

street_summary_K <- Street_summary(street_all_sites_3, "Saltaire Titus")

## "Silsden Hothfield" 

street_summary_L <- Street_summary(street_all_sites_3, "Silsden Hothfield")

## Joining the streets together in one table

street_summary_combine <- rbind(street_summary_A, street_summary_B, street_summary_C, street_summary_D, street_summary_E, street_summary_F, street_summary_G, street_summary_H, street_summary_I, street_summary_J, street_summary_K, street_summary_L)

street_summary_combine[ , 2:7] <- round(street_summary_combine[ , 2:7], 2)


## Applying the Kable function to the dataframe

street_summary_combine %>%
  kbl(caption = "Summary table of air pollutants by school street") %>%
  kable_paper("hover") %>%
  kable_classic_2(full_width = F, html_font = "Cambria")
Summary table of air pollutants by school street
Site mean_NO mean_NO2 mean_O3 mean_PM1 mean_PM10 mean_PM25
Apperley Bridge marina 7.54 27.45 57.99 4.18 9.11 6.08
Cavendish Prim Sch Hall Rd 5.11 7.67 63.42 4.46 9.30 6.33
East Bowling Flockton Grove 13.10 16.78 60.05 4.87 9.94 6.74
Farfield PRim Sch Reevy 7.82 5.48 66.34 4.49 9.66 6.41
Girlington St Leonards Road 12.42 2.96 57.20 4.77 9.59 6.54
Gt Horton All Saints Primary School 6.69 7.14 64.70 4.65 9.39 6.51
Horton Aberdeen Place 6.69 7.14 64.70 4.65 9.39 6.51
Ilkley Grove Road 4.87 1.45 51.90 3.39 7.49 4.96
Keighley 12.28 6.71 45.41 4.72 9.33 6.07
Low Moor First 17.46 5.71 44.62 5.10 10.19 6.91
Saltaire Titus 5.64 4.49 52.34 3.83 8.45 5.73
Silsden Hothfield 7.02 1.94 46.79 1.87 6.75 4.17 
# Using a cluster bar chart to give  visual representation of the mean distribution of all the pollutants by school street

## First bring all the mean into one table

street_summary_combine_2 <- street_summary_combine %>%
  gather("mean_NO", "mean_NO2", "mean_O3",  "mean_PM1", "mean_PM10",    "mean_PM25", key = "Pollutants", value = "Mean of pollutants")

## plot the bar chart

ggplot(street_summary_combine_2) +
  geom_bar(stat = "identity", aes(x = reorder(Site, `Mean of pollutants`), y = `Mean of pollutants`, fill = Pollutants)) +
  coord_flip() +
  scale_fill_brewer(palette = "Set2")

5 Statistical trend analysis

This study employs time series analysis to investigate the influence of School Streets road closures on air quality surrounding identified schools. Due to the unavailability of pre-intervention data, establishing baseline concentrations for comparison was not feasible. Consequently, the analysis focuses on examining differences in the diurnal profile of pollutant concentrations across various monitoring sites during periods of School Streets road closures.

Drawing on the methodology employed in the London School Streets Air Quality Monitoring study, the impact of interventions is assessed by comparing pollutant concentrations when School Streets were closed against times when they were not. Notably, unlike the London study, this analysis lacks monitoring sites without interventions. Therefore, comparisons are made between periods of intervention and non-intervention, providing insights into the unique air quality dynamics surrounding the participating schools.

By aligning the methodology with established frameworks while adapting it to the research context, the aim is to provide valuable insights into the efficacy of School Streets interventions in mitigating air pollution. This comparative approach enhances the robustness of the findings and contributes to the growing body of literature on urban air quality management strategies.

According to the City of the Bradford Metropolitan Council, the current opening and closing time are 08:45am and 03:00pm respectively. Therefore giving ample drop off time and pickup time before and after closing time, and taking reference from the London School Streets Air quality Monitoring study and insights from a local study, the intervention times have been estimated to be from 7:45am to 9:15am for drop-offs and from 2:30pm to 4:30pm for pick-ups, with the intention of refining these times as the data is analyzed.

Due to further investigation of each school’s starting and closing time, the following school had a different School Street intervention period due to a different closing and/or opening time.

  1. Cavendish Primary School; start time 7:45am

5.1 Average Diurnal Profiles of Pollutants at Each School Site during School term

This analysis presents the average diurnal profiles of pollutants measured at various sites. The methodology involves averaging pollutant concentrations across timestamps, resulting in a single average concentration for each time of day. Specifically, concentrations measured at 7:15am, for instance, are aggregated to derive a representative average concentration for that time slot. This averaging process is restricted to term times, with data from half-term weeks, weekends and school breaks excluded from the analysis.

To estimate term times, school term time was collected from the three schools including Cavendish Primary School, Farfield Primary School. Additionally, school termtime from All Saints CofE Primary School in GT Horton was collected. Access to term time data prior to September resumption was not always feasible. The estimated term times for schools within a one-mile radius of the school street are as follows:

School opening: June 5th to July 21st School closed: July 24th to September 1st School reopened: September 5th to September 20th

These estimated term times serve as the basis for the diurnal profile analysis, allowing for the identification of temporal patterns in pollutant concentrations during school term periods.

# Filter data to include only term times from June 8th to July 21st and September 5th to September 20th in the dataset 

street_term_time <- street_all_sites_3 %>%
  filter(Date >= "2023-06-08" & Date <= "2023-07-22" | Date >= "2023-09-05" & Date <= "2023-09-21")

# Here I am going to separate the hour column into two columns retaining only the column with the hour

street_term_time <- street_term_time %>%
  separate(Hour, into = c("Hour", "Minutes"), sep = ":")

# Removing the minutes column 

street_term_time <- subset(street_term_time, select = -Minutes)

# There are missing values under hour that need to be converted to hour 23

street_term_time$Hour[is.na(street_term_time$Hour)] <- 23

# Group data by school and timestamp, calculate mean pollutant concentration

street_term_time_2 <- street_term_time %>%
  group_by(Site, Hour) %>%
  summarise(
    Mean_NO = mean(NO),
    Mean_NO2 = mean(`NO₂`),
    Mean_O3 = mean(`O₃`),
    Mean_PM1 = mean(PM1),
    Mean_PM10 = mean(PM10),
    Mean_PM2.5 = mean(PM2.5)
  )

# Convert time to factor for proper ordering on x-axis

street_term_time_2$Hour <- factor(street_term_time_2$Hour, levels = unique(street_term_time_2$Hour))

# Gathering all the mean pollutant in one column

street_term_time_2 <- street_term_time_2 %>%
  gather("Mean_NO", "Mean_NO2", "Mean_O3",  "Mean_PM1", "Mean_PM10",    "Mean_PM2.5", key = "Pollutants", value = "Mean of pollutants")

# Plot average diurnal profiles for each pollutant using ggplot
## Creating a function for all the plots

 street_diurnal_plot <- function(df, Sites, Plot_title) {
   df %>%
  filter(Site == Sites) %>%
  ggplot() +
  geom_line(aes(x = Hour, y = `Mean of pollutants`, color = Pollutants, group = Pollutants), linetype = "dashed", size = 0.8, alpha = 10) +
  labs(title = Plot_title, x = "Time of Day", y = "Mean Pollutant Concentration") +
    scale_color_manual(values = c("orange", "blue", "red", "brown", "purple", "green")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 0, vjust = 0.1, hjust = 0.1, size = 15), axis.text.y = element_text(size = 10), axis.title = element_text(size = 15), plot.title = element_text(size = 20), legend.text = element_text(size = 15), legend.title = element_text(size = 15))
 }
 
## Creating a plot for all the school streets

## Apperley Bridge marina

plot_A <- street_diurnal_plot(street_term_time_2, "Apperley Bridge marina", "Apperley Bridge marina")

## Cavendish

plot_B <- street_diurnal_plot(street_term_time_2, "Cavendish Prim Sch Hall Rd", "Cavendish Prim Sch Hall Rd")

## East Bowling

plot_C <- street_diurnal_plot(street_term_time_2, "East Bowling Flockton Grove", "East Bowling Flockton Grove")

## Farfield

plot_D <- street_diurnal_plot(street_term_time_2, "Farfield PRim Sch Reevy", "Farfield PRim Sch Reevy")

## Girlington

plot_E <- street_diurnal_plot(street_term_time_2, "Girlington St Leonards Road", "Girlington St Leonards Road")

## "Gt Horton All Saints Primary School"

plot_F <- street_diurnal_plot(street_term_time_2, "Gt Horton All Saints Primary School", "Gt Horton All Saints Primary School")

## "Horton Aberdeen Place"

plot_G <- street_diurnal_plot(street_term_time_2, "Horton Aberdeen Place", "Horton Aberdeen Place")

## "Ilkley Grove Road"

plot_H <- street_diurnal_plot(street_term_time_2, "Ilkley Grove Road", "Ilkley Grove Road")

## "Keighley"

plot_I <- street_diurnal_plot(street_term_time_2, "Keighley", "Keighley")

## "Low Moor First" 

plot_J <- street_diurnal_plot(street_term_time_2, "Low Moor First", "Low Moor First")

## "Saltaire Titus" 

plot_K <- street_diurnal_plot(street_term_time_2, "Saltaire Titus", "Saltaire Titus")

## "Silsden Hothfield" 

plot_L <- street_diurnal_plot(street_term_time_2, "Silsden Hothfield", "Silsden Hothfield")

grid.arrange(plot_A, plot_B, plot_C, plot_D, plot_E, plot_F, plot_G, plot_H, plot_I, plot_J, plot_K, plot_L, ncol = 2)

############### OR, using facet wrap within ggplot to arrange the plots

ggplot(street_term_time_2) +
  geom_line(aes(x = Hour, y = `Mean of pollutants`, color = Pollutants, group = Pollutants), linetype = "dashed", size = 0.8, alpha = 15) +
  labs(title = "Average Diurnal Profile of Pollutant Concentration", x = "Time of Day", y = "Mean NO Concentration") +
  facet_wrap(~ Site, nrow = 4) +
  scale_color_manual(values = c("orange", "blue", "red", "brown", "purple", "green")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.1, hjust = 0.1, size = 13), axis.text.y = element_text(size = 20), axis.title = element_text(size = 15), plot.title = element_text(size = 20), legend.text = element_text(size = 30), legend.title = element_text(size = 30), strip.text = element_text(size = 18), panel.grid.major = element_line(size = 1.5)) +
   guides(color = guide_legend(override.aes = list(size = 25)))

From the graphs above, there is not a distinct drop in air pollutants for any of the diurnal profiles during the first term time of the three months period of the survey so far.

It should be noted that the morning road closures typically coincide with the peak traffic period, while the afternoon closures do not, occurring well before 5pm. As a result, it would be reasonable to expect the afternoon road closures to have less effect on pollutant concentrations than those in the morning.

5.2 Average Diurnal Profiles of Pollutants at Each School Site Throughout the Three-Month Period

This analysis explores the average diurnal profiles of pollutants observed during the entire three-month duration of the school street intervention.

# Here I am going to separate the hour column into two columns retaining only the column with the hour

street_entire_period <- street_all_sites_3 %>%
  separate(Hour, into = c("Hour", "Minutes"), sep = ":")

# Removing the minutes column 

street_entire_period <- subset(street_entire_period, select = -Minutes)

# There are missing values under hour that need to be converted to hour 23

street_entire_period$Hour[is.na(street_entire_period$Hour)] <- 23

# Group data by school and timestamp, calculate mean pollutant concentration

street_entire_period_2 <- street_entire_period %>%
  group_by(Site, Hour) %>%
  summarise(
    Mean_NO = mean(NO, na.rm = TRUE),
    Mean_NO2 = mean(`NO₂`, na.rm = TRUE),
    Mean_O3 = mean(`O₃`, na.rm = TRUE),
    Mean_PM1 = mean(PM1, na.rm = TRUE),
    Mean_PM10 = mean(PM10, na.rm = TRUE),
    Mean_PM2.5 = mean(PM2.5, na.rm = TRUE)
  )
View(street_entire_period_2)

# Convert time to factor for proper ordering on x-axis

street_entire_period_2$Hour <- factor(street_entire_period_2$Hour, levels = unique(street_entire_period_2$Hour))

# Gathering all the mean pollutant in one column

street_entire_period_2 <- street_entire_period_2 %>%
  gather("Mean_NO", "Mean_NO2", "Mean_O3",  "Mean_PM1", "Mean_PM10",    "Mean_PM2.5", key = "Pollutants", value = "Mean of pollutants")

# Using ggplot to get the graph
street_entire_period_2[street_entire_period_2$Site == "Farfield PRim Sch Reevy", ]
  # A tibble: 144 × 4
  # Groups:   Site [1]
     Site                    Hour  Pollutants `Mean of pollutants`
     <chr>                   <fct> <chr>                     <dbl>
   1 Farfield PRim Sch Reevy 00    Mean_NO                   10.0 
   2 Farfield PRim Sch Reevy 01    Mean_NO                    9.40
   3 Farfield PRim Sch Reevy 02    Mean_NO                    8.49
   4 Farfield PRim Sch Reevy 03    Mean_NO                    7.81
   5 Farfield PRim Sch Reevy 04    Mean_NO                    7.63
   6 Farfield PRim Sch Reevy 05    Mean_NO                    7.19
   7 Farfield PRim Sch Reevy 06    Mean_NO                    6.26
   8 Farfield PRim Sch Reevy 07    Mean_NO                    6.62
   9 Farfield PRim Sch Reevy 08    Mean_NO                    5.03
  10 Farfield PRim Sch Reevy 09    Mean_NO                    3.97
  # ℹ 134 more rows
ggplot(street_entire_period_2) +
  geom_line(aes(x = Hour, y = `Mean of pollutants`, color = Pollutants, group = Pollutants), linetype = "dashed", size = 0.8, alpha = 15) +
  labs(title = "Average Diurnal Profile of Pollutant Concentration", x = "Time of Day", y = "Mean NO Concentration") +
  facet_wrap(~ Site, nrow = 4) +
  scale_color_manual(values = c("orange", "blue", "red", "brown", "purple", "green")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.1, hjust = 0.1, size = 13), axis.text.y = element_text(size = 20), axis.title = element_text(size = 15), plot.title = element_text(size = 20), legend.text = element_text(size = 30), legend.title = element_text(size = 30), strip.text = element_text(size = 18), panel.grid.major = element_line(size = 1.5)) +
   guides(color = guide_legend(override.aes = list(size = 25)))

6 Spatial Distribution of the pollutants across the Wards